About the Author
Cynthia Gibas is an assistant professor of biology at Virginia Tech, in Blacksburg, Virginia. She’s been a computational biologist since before computational biology was cool, and is currently learning to drive her spankin’ new home-built Linux cluster. Her research interests include the structure and evolution of genomes, the properties of protein surfaces and interfaces, and prediction of protein structure. She teaches introductory courses in bioinformatics methods for biologists and is looking forward to her next real vacation, sometime in 2006.
Per Jambeck is a Ph.D. student in the bioengineering department at the University of California, San Diego. He has worked on computational biology problems since 1994, concentrating on machine learning applications in understanding multidimensional biological data. Per smiles wistfully at the mention of free time, but he manages to host shows at community and student-run radio stations anyway.
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Excerpt of Chapter 1 Biology in the Computer Age From the interaction of species and populations, to the function of tissues and cells within an individual organism, biology is defined as the study of living things. In the course of that study, biologists collect and interpret data. Now, at the beginning of the 21st century, we use sophisticated laboratory technology that allows us to collect data faster than we can interpret it. We have vast volumes of DNA sequence data at our fingertips. But how do we figure out which parts of that DNA control the various chemical processes of life? We know the function and structure of some proteins, but how do we determine the function of new proteins? And how do we predict what a protein will look like, based on knowledge of its sequence? We understand the relatively simple code that translates DNA into protein. But how do we find meaningful new words in the code and add them to the DNA-protein dictionary? Bioinformatics is the science of using information to understand biology; it’s the tool we can use to help us answer these questions and many others like them. Unfortunately, with all the hype about mapping the human genome, bioinformatics has achieved buzzword status; the term is being used in a number of ways, depending on who is using it. Strictly speaking, bioinformatics is a subset of the larger field of computational biology , the application of quantitative analytical techniques in modeling biological systems. In this book, we stray from bioinformatics into computational biology and back again. The distinctions between the two aren’t important for our purpose here, which is to cover a range of tools and techniques we believe are critical for molecular biologists who want to understand and apply the basic computational tools that are available today. The field of bioinformatics relies heavily on work by experts in statistical methods and pattern recognition. Researchers come to bioinformatics from many fields, including mathematics, computer science, and linguistics. Unfortunately, biology is a science of the specific as well as the general. Bioinformatics is full of pitfalls for those who look for patterns and make predictions without a complete understanding of where biological data comes from and what it means. By providing algorithms, databases, user interfaces, and statistical tools, bioinformatics makes it possible to do exciting things such as compare DNA sequences and generate results that are potentially significant. “Potentially significant” is perhaps the most important phrase. These new tools also give you the opportunity to overinterpret data and assign meaning where none really exists. We can’t overstate the importance of understanding the limitations of these tools. But once you gain that understanding and b! ecome an intelligent consumer of bioinformatics methods, the speed at which your research progresses can be truly amazing. How Is Computing Changing Biology?
An organism’s hereditary and functional information is stored as DNA, RNA, and proteins, all of which are linear chains composed of smaller molecules. These macromolecules are assembled from a fixed alphabet of well-understood chemicals: DNA is made up of four deoxyribonucleotides (adenine, thymine, cytosine, and guanine), RNA is made up from the four ribonucleotides (adenine, uracil, cytosine, and guanine), and proteins are made from the 20 amino acids. Because these macromolecules are linear chains of defined components, they can be represented as sequences of symbols. These sequences can then be compared to find similarities that suggest the molecules are related by form or function. Sequence comparison is possibly the most useful computational tool to emerge for molecular biologists. The World Wide Web has made it possible for a single public database of genome sequence data to provide services through a uniform interface to a worldwide community of users. With a commonly used computer program called fsBLAST, a molecular biologist can compare an uncharacterized DNA sequence to the entire publicly held collection of DNA sequences. In the next section, we present an example of how sequence comparison using the BLAST program can help you gain insight into a real disease. The Eye of the Fly Fruit flies (Drosophila melanogaster) are a popular model system for the study of development of animals from embryo to adult. Fruit flies have a gene called eyeless, which, if it’s “knocked out (i.e., eliminated from the genome using molecular biology methods), results in fruit flies with no eyes. It’s obvious that the eyeless gene plays a role in eye development. Researchers have identified a human gene responsible for a condition called aniridia. In humans who are missing this gene (or in whom the gene has mutated just enough for its protein product to stop functioning properly), the eyes develop without irises. If the gene for aniridia is inserted into an eyeless drosophila “knock out,” it causes the production of normal drosophila eyes. It’s an interesting coincidence. Could there be some similarity in how eyeless and aniridia function, even though flies and humans are vastly different organisms? Possibly. To gain insight into how eyeless and aniridia work together, we can compare their sequences. Always bear in mind, however, that genes have complex effects on one another. Careful experimentation is required to get a more definitive answer. As little as 15 years ago, looking for similarities between eyeless and aniridia DNA sequences would have been like looking for a needle in a haystack. Most scientists compared the respective gene sequences by hand-aligning them one under the other in a word processor and looking for matches character by character. This was time-consuming, not to mention hard on the eyes. In the late 1980s, fast computer programs for comparing sequences changed molecular biology forever. Pairwise comparison of biological sequences is the foundation of most widely used bioinformatics techniques. Many tools that are widely available to the biology community–including everything from multiple alignment, phylogenetic analysis, motif identification, and homology-modeling software, to web-based database search services–rely on pairwise sequence-comparison algorithms as a core element of their function.
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